Research on Performance Evaluation and Improvement of Deep Reinforcement Learning in Traffic Scene Target Detection

As deep reinforcement learning enters a new era, object localization in traffic scenes has become increasingly important. This paper proposes an active detection model of traffic objects based on a deep reinforcement learning algorithm to optimize target detection performance. By constructing a deep Q network, target locations are positioned for specific traffic object categories, and top-down target reasoning is achieved by learning a series of specific conversion actions. During the implementation process, the image data set is used to train the model and analyze the action space, state space and reward function. Experimental results show that the model exhibits high accuracy and performance in positioning traffic lights and traffic speed limit signs. This paper summarizes that the active detection model of traffic objects based on deep reinforcement learning has good target detection capabilities, performance, and potential application prospects in traffic scenarios.